About ADFM 2026
The rapid advancement of foundation models in fields like healthcare, cybersecurity, and finance highlights the urgent need to improve their anomaly detection capabilities. Despite their growing application in high-stakes areas, the challenges of using these models for anomaly detection remain underexplored. The Anomaly Detection with Foundation Models (ADFM 2026) workshop aims to address this gap by focusing on the intersection of foundation models and anomaly detection. Our organizing and technical committee, composed of leading experts, provides a platform for advancing research and discussing the recent breakthroughs, and the technical and ethical implications of deploying these models. ADFM 2026 will foster interdisciplinary collaboration and contribute to the development of more reliable and effective anomaly detection systems in artificial intelligence.
Where
Colorado Convention Center, Denver, CO, USA
When
June 3 - 4, 2026
ADFM 2026 Schedule
[in Denver local time]
[More info about the schedule will be updated here]
Submissions
Submission Instructions
We welcome full paper submissions. The papers must be no longer than 8 pages in total (excluding references). Please submit at the following CMT website:
ADFM 2026 CMT submission website.
Submission deadline: February 27, 2026 11:59 PM EDT
Notification to authors: March 20, 2026
Camera ready deadline: April 10, 2026
We invite the submission of original and high-quality research papers in the topics related to anomaly detection with foundation models.
We're seeking dedicated Reviewers! Please self-nominate via the
reviewer self-nomination form. Thanks for your support!
Topics
The topics for ADFM 2026 include, but are not limited to:
- Fundamental theories and principles of foundation models for anomaly detection.
- Advanced anomaly detection algorithms and frameworks utilizing foundation models.
- Sector-specific anomaly detection employing foundation models, covering areas such as finance, healthcare, cybersecurity, and industrial systems.
- Evaluation standards and benchmarks for appraising anomaly detection in foundation models.
- Methods enhancing the clarity and comprehensibility of foundation models in anomaly detection.
- Methods promoting fairness and diminishing bias in anomaly detection with foundation models.
- Privacy-enhancing techniques in anomaly detection with foundation models.
- Trust and reliability of foundation models in crucial anomaly detection applications.
- Interdisciplinary methods for refining anomaly detection, incorporating insights from fields like psychology and sociology.
- Adaptive learning and adjustment mechanisms for foundation models in dynamic settings.
- Integration of expert knowledge and domain-specific systems with foundation models for enhanced anomaly detection.
- Exploratory discussions on the constraints and challenges of current foundation models in identifying anomalies in complex and noisy datasets.
- Prospective insights on the evolution of anomaly detection methods with the advancement of foundation models.
ADFM 2026 Venue
Colorado Convention Center, Denver, CO, USA
ADFM 2026 will be held at the Colorado Convention Center, Denver, CO, USA on June 3 - 4, 2026.
Organizers
Kuan-Chuan Peng
Mitsubishi Electric Research Laboratories (MERL)
Ying Zhao
Ricoh Software Research Center (Beijing) Co.,Ltd.
Abhishek Aich
NEC Laboratories, America
Steering Committee
Xian Tao
Chinese Academy of Sciences
Wenbing Zhu
Fudan University and Rongcheer




